Detailed explanation of array creation in Python NumPy tutorial

This article brings you relevant knowledge about Python. It mainly introduces the creation method of arrays in Python NumPy in detail. The sample code in the article is explained in detail, which is helpful for us to learn Python. Please refer to it if you need help.

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Use List to create an array

Arrays are used in a Multiple values ​​are stored in variables. Python does not have built-in support for arrays, but Python lists can be used instead.

Example:

arr = [1, 2, 3, 4, 5]
arr1 = ["geeks", "for", "geeks"]

# 用于创建数组的 Python 程序
 
# 使用列表创建数组
    arr=[1, 2, 3, 4, 5]
    for i in arr:
        print(i)

Output:

1
2
3
4
5

Use the array function to create an array

array(data type, value list) The function is used to create an array, specified in its parameters List of data types and values.

Example:

# 演示 array() 工作的 Python 代码
  
# 为数组操作导入“array”
import array
  
# 用数组值初始化数组
# 用有符号整数初始化数组
arr = array.array('i', [1, 2, 3]) 
 
# 打印原始数组
print ("The new created array is : ",end="")
for i in range (0,3):
    print (arr[i], end=" ")
 
print ("\r")

Output:

##The new created array is : 1 2 3 1 5

numpy.empty(shape, dtype = float, order = 'C'): Returns a new array of the given shape and type, with random values.

# 说明 numpy.empty 方法的 Python 代码
 
import numpy as geek
 
b = geek.empty(2, dtype = int)
print("Matrix b : \n", b)
 
a = geek.empty([2, 2], dtype = int)
print("\nMatrix a : \n", a)
 
c = geek.empty([3, 3])
print("\nMatrix c : \n", c)

Output:

Matrix b :

[ 0 1079574528]

Matrix a :
[[0 0 ]
[0 0]]

Matrix a :
[[ 0. 0. 0.]
[ 0. 0. 0.]
[ 0. 0. 0 .]]

numpy.zeros(shape, dtype = None, order = 'C'): Returns a new array of the given shape and type, with zeros.

# 说明 numpy.zeros 方法的 Python 程序
 
import numpy as geek
 
b = geek.zeros(2, dtype = int)
print("Matrix b : \n", b)
 
a = geek.zeros([2, 2], dtype = int)
print("\nMatrix a : \n", a)
 
c = geek.zeros([3, 3])
print("\nMatrix c : \n", c)

Output:

Matrix b :

[0 0]

Matrix a :
[[0 0 ]
[0 0]]

Matrix c :
[[ 0. 0. 0.]
[ 0. 0. 0.]
[ 0. 0. 0 .]]

reshape method to reshape the array. Consider an array of shape (a1, a2, a3, ..., aN). We can reshape and convert it into another array of shape (b1, b2, b3, ..., bM).

numpy.reshape(array, shape, order = 'C'): Reshape the array without changing the array data .

# 说明 numpy.reshape() 方法的 Python 程序
 
import numpy as geek
 
array = geek.arange(8)
print("Original array : \n", array)
 
# 具有 2 行和 4 列的形状数组
array = geek.arange(8).reshape(2, 4)
print("\narray reshaped with 2 rows and 4 columns : \n", array)
 
# 具有 2 行和 4 列的形状数组
array = geek.arange(8).reshape(4 ,2)
print("\narray reshaped with 2 rows and 4 columns : \n", array)
 
# 构造 3D 数组
array = geek.arange(8).reshape(2, 2, 2)
print("\nOriginal array reshaped to 3D : \n", array)

Output:

Original array :

[0 1 2 3 4 5 6 7]

array reshaped with 2 rows and 4 columns :
[[0 1 2 3]
[4 5 6 7]]

array reshaped with 2 rows and 4 columns :
[[0 1]
[2 3]
[4 5]
[6 7]]

Original array reshaped to 3D :
[[[0 1]
[2 3]]

[[4 5]
[6 7]]]

arange Returns uniformly distributed values ​​within a given interval. StepThe length is specified.

linspace Returns uniformly distributed values ​​within a given interval. The element numbered _ is returned.

arange([start,] stop[, step,][, dtype]): Returns an array with evenly spaced elements based on the interval. The intervals mentioned are half-open, i.e. [start, stop]

# 说明 numpy.arange 方法的 Python 编程
 
import numpy as geek
 
print("A\n", geek.arange(4).reshape(2, 2), "\n")
 
print("A\n", geek.arange(4, 10), "\n")
 
print("A\n", geek.arange(4, 20, 3), "\n")

Output:

A

[[0 1]
[2 3]]

A
[4 5 6 7 8 9]

A
[ 4 7 10 13 16 19]

numpy.linspace(start, stop, num = 50, endpoint = True, retstep = False, dtype = None): Returns numeric space evenly across intervals. Like arange but instead of step it uses sample numbers.

# 说明 numpy.linspace 方法的 Python 编程
 
import numpy as geek
 
# 重新设置为 True
print("B\n", geek.linspace(2.0, 3.0, num=5, retstep=True), "\n")
 
# 长期评估 sin()
x = geek.linspace(0, 2, 10)
print("A\n", geek.sin(x))

Output:

B

(array([ 2. , 2.25, 2.5 , 2.75, 3. ]), 0.25)

A
[0. 929743]

Flat array

We can use the flatten method to make a copy of the array Folded into one dimension. It accepts an order parameter. The default value is "C" (for row-major order). Use "F" for column major order.

: Returns a copy of the array folded into one dimension.

# 说明 numpy.flatten() 方法的 Python 程序
 
import numpy as geek
 
array = geek.array([[1, 2], [3, 4]])
 
# 使用扁平化方法
array.flatten()
print(array)
 
#使用扁平化方法
array.flatten('F')
print(array)